Get Full Test Results

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide openWorldHint: true, but the description adds significant behavioral context: pagination groups results by table, and it supports filtering failures and different response formats. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph covering key points without unnecessary detail. It is well-structured and front-loaded with the tool's core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description provides sufficient detail about return values (testCases grouped by table), pagination behavior, filtering, and prerequisites. It covers all essential aspects for correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the description goes beyond the schema by explaining pagination applies to tables, not test cases, and that projectId should come from openl_list_projects(). It also clarifies response_format options.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves full test execution results with pagination support, and specifies it returns a summary including testCases array grouped by table. This differentiates it from sibling tools like openl_get_test_results_by_table and openl_get_test_results_summary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs users to call openl_start_project_tests() first, and explains that pagination applies to test tables, not individual test cases. It does not explicitly state when not to use this tool, but the context is clear enough for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.